Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning

Multi-Agent Optimizing Traffic Light Signals Using Deep Reinforcement Learning

In recent years, rapid urbanization has led to increased traffic congestion, rendering traditional traffic light control methods ineffective. Deep Reinforcement Learning (DRL) has emerged as a promising approach to sequential decision-making, offering adaptive and efficient solutions for traffic man...

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Bibliographic Details
Main Authors: Zahra Fereidooni, Luciano Alessandro Ipsaro Palesi, Paolo Nesi
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Deep reinforcement learning
smart cities
traffic control
traffic light
multi-agent
single-agent
Online Access:https://ieeexplore.ieee.org/document/11029249/
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